Product Data Scientist

encora10; Mexico; On-site; PHP 2,950,000 - 4,425,000; Full time

Job summary

Coforge is seeking a Product Data Scientist to join our Home Improvement lending product team. You will be embedded with Product Management, turning product questions into structured analyses and actionable recommendations.

The role involves designing experiments, defining metrics, and building data pipelines to support underwriting, borrower experience, and growth decisions. A strong analytics background and SQL/Python or R are essential.

Qualifications

• 5+ years in hybrid analytics/data science roles directly informing product decisions.
• Strong grounding in statistics, experimentation, and causal inference.
• Fluent in SQL and Python or R; familiar with data engineering fundamentals.

Responsibilities

• Partner day-to-day with Home Improvement Product Managers as embedded data science resource.
• Design, run, and interpret experiments (A/B and quasi-experimental) across the borrower funnel.
• Define and own metrics framework for the Home Improvement product line and instrumentation.
• Collaborate with engineering to ensure event tracking and data quality.
• Build and maintain data pipelines and models for analyses and collaboration with data engineering.
• Develop and validate statistical and ML models to support product decisions with fairness and explainability.
• Leverage AI tools to accelerate analyses while ensuring human validation for decisions.
• Communicate findings with clear write-ups and visuals tied to product decisions.
• Contribute to PI planning by sizing opportunities and assessing measurement risks.
• Monitor product and experiment performance post-launch and surface anomalies.

Skills

Python

SQL

Data analysis

Education

Bachelor's degree or higher in a quantitative field

Job description

Job Title: Product Data Scientist

Key Skills: Python or R, SQL, Data

Experience: 6+ years

Location Hermosillo or Mexico City

Mode: Onsite

We at Coforge are hiring Position (17739-PE-MX-TM2-6) with the following skill set.

This role sits at the intersection of product analytics, experimentation, and data science - embedded directly with Product Management to help shape and grow our Home Improvement lending product. You'll be the analytical backbone for a product team making high-stakes decisions about underwriting funnels, borrower experience, and growth, translating messy data into clear evidence and, where it counts, into models and instrumentation that ship. It's a high-impact role for someone who is equally comfortable running a rigorous A/B test, writing a dbt model, and explaining a lift curve to a VP.

Responsibilities

• Partner day-to-day with Home Improvement Product Managers as their embedded data science and analytics resource - turning open-ended product questions into structured analyses and clear recommendations
• Design, run, and interpret experiments (A/B and quasi-experimental) across the borrower funnel - from offer presentment through origination - with rigor around power, sample ratio mismatch, novelty effects, and interaction risk across concurrent tests
• Define and own the metrics framework for the Home Improvement product line: north-star and guardrail metrics, funnel and cohort definitions, and the instrumentation needed to measure them reliably
• Work with engineering to ensure event tracking and logging are complete, accurate, and well-documented at the point of instrumentation, not discovered as gaps after the fact
• Build and maintain data pipelines and models (e.g., SQL/dbt transformations, feature pipelines) well enough to be self-sufficient for most analyses and to collaborate credibly with data engineering on the rest
• Develop and validate statistical and ML models supporting product decisions - response/propensity models, funnel drop-off and conversion models, segmentation, and early-stage risk or pricing signals in partnership with credit strategy - with attention to fairness, explainability, and regulatory context appropriate to a lending business
• Bring AI fluency to the work: use LLM- and agentic-tooling to accelerate exploratory analysis, requirements gathering, and documentation, while knowing where automated outputs need human judgment and validation before they inform a decision
• Communicate findings in a way that drives action - clear write-ups, well-chosen visualizations, and recommendations tied to specific product or roadmap decisions, not just descriptive dashboards
• Contribute to PI planning and roadmap discussions by sizing opportunities, flagging measurement risk in proposed initiatives, and helping the team commit to work that can actually be evaluated
• Continuously monitor product and experiment performance post-launch, and proactively surface anomalies, regressions, or new opportunities rather than waiting to be asked

Required Qualifications

• 5+ years of experience in a hybrid analytics/data science role (e.g., analytics consulting, product data science, applied statistics) with a track record of directly informing product decisions; bachelor's degree or higher in a quantitative field, or equivalent combination of education and experience
• You have strong grounding in statistics and experimentation - hypothesis testing, causal inference, experiment design, and you can explain the difference between a significant result and a meaningful one
• You're fluent in SQL and at least one scripting/statistical language (Python or R), and you're comfortable enough with data engineering fundamentals (pipelines, transformations, data modeling) to build what you need and partner effectively with engineers on the rest
• You can develop, validate, and communicate the tradeoffs of statistical and machine learning models, and you know when a simpler model or a well-designed experiment beats a complex one
• You use AI tools in your day-to-day work - for exploratory analysis, documentation, and accelerating routine analytics - and you know when their outputs need scrutiny before they touch a product decision
• You think like a consultant: you get to the real question behind the question, structure ambiguous problems, and land on recommendations stakeholders can act on
• You have good judgment about rigor versus speed, and you don't cut corners on measurement integrity just to hit a deadline
• You're a clear communicator who can flex between a technical conversation with engineering and a decision-focused conversation with product and business stakeholders
• You're curious about how data, experimentation, and AI can change what's possible in consumer lending products, and you're always looking for a better way to answer the question

Nice to have

• Background in fintech, consumer lending, or home improvement/contractor financing
• Experience with CDP platforms, event instrumentation